Developed block-wise INT8 quantization support for DeepSeek V3/R1 models in the fzyzcjy/sglang repository, focusing on improving inference speed and cost efficiency for deployed deep learning systems. The work involved designing and implementing new quantization methods and custom CUDA kernels using C++ and Python, targeting model optimization and quantization challenges. Comprehensive tests were created to validate both the accuracy and performance gains of the new approach, ensuring deployment readiness. The technical depth included careful validation of inference throughput and correctness, with detailed documentation to support integration. This contribution addressed the need for efficient, scalable inference in production environments.